# Ocr Captcha

URL: https://interfaze.ai/models/xiaolvocr-captcha

[All models](https://interfaze.ai/models)

Ocr Captcha by xiaolv, a image-to-text model with OCR capabilities. Understand and compare OCR features, benchmarks, and capabilities.

## Comparison

| Feature | Ocr Captcha | Interfaze |
| --- | --- | --- |
| Input Modalities | image | image, text, audio, video, document |
| Native OCR | Yes | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | unknown | 1M |
| Tool Calling | No | Tool calling supported + built in browser, code execution and web search |

### OCR Capabilities

| Feature | Ocr Captcha | Interfaze |
| --- | --- | --- |
| Text Bounding Boxes | No | Yes |
| Confidence Scores | No | Yes |
| Dense Image Processing | No | Yes |
| Low Quality Images | No | Yes |
| Handwritten Text | No | Yes |
| Charts, Tables & Equations | No | Yes |

### Scaling

| Feature | Ocr Captcha | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/xiaolv/ocr-captcha)

## 介绍（Introduction）

\*\*验证码识别模型（ocr-captcha）\*\*专门识别常见验证码的模型，训练模型有2个：

1.**small**:训练数据大小为700MB，约8.4万张验证码图片，训练轮次27轮，最终的精度将近100%，**推荐下载这个模型**；

2.**big**:训练数据大小为11G，约135万个验证码图片，训练轮次1轮，最终的精度将近93.95%(由于资源问题，无法训练太久)；

## 数据分布

1.**类型**：1. 纯数字型；2. 数字+字母型；3.纯字母型（大小写）

2.**长度**：4位、5位、6位

## 数据微调

1.**基座模型**：基座模型参考达摩院发布的[读光-文字识别-行识别模型-中英-通用领域](https://www.modelscope.cn/models/damo/cv_convnextTiny_ocr-recognition-general_damo/summary)

2.**具体微调参考以上链接**

## 模型体验链接

modelscope：[验证码识别模型（ocr-captcha）](https://modelscope.cn/studios/xiaolv/ocr/summary)

## 单独模型链接（modelscope）

1.**[验证码识别模型（小）-small](https://modelscope.cn/models/xiaolv/ocr_small/summary)**

2.**[验证码识别模型（大）-big](https://modelscope.cn/models/xiaolv/ocr_big/summary)**

## 快速使用（Quickstart）

代码提供web网页版：`myself_train_model.py`

```
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import gradio as gr
import os


class xiaolv_ocr_model():

    def __init__(self):
        model_small = r"./output_small"
        model_big = r"./output_big"
        self.ocr_recognition_small = pipeline(Tasks.ocr_recognition, model=model_small)
        self.ocr_recognition1_big = pipeline(Tasks.ocr_recognition, model=model_big)


    def run(self,pict_path,moshi = "small", context=[]):
        pict_path = pict_path.name
        context = [pict_path]

        if moshi == "small":
            result = self.ocr_recognition_small(pict_path)
        else:
            result = self.ocr_recognition1_big(pict_path)

        context += [str(result['text'][0])]
        responses = [(u, b) for u, b in zip(context[::2], context[1::2])]
        print(f"识别的结果为：{result}")
        os.remove(pict_path)
        return responses,context




if __name__ == "__main__":
    pict_path = r"C:\Users\admin\Desktop\图片识别测试\企业微信截图_16895911221007.png"
    ocr_model = xiaolv_ocr_model()
    # ocr_model.run(pict_path)
```

## 联系我们（Contact Us）

如果你想给我们的研发团队和产品团队留言，请通过邮件（[2240560729@qq.com](mailto:2240560729@qq.com)）联系我们。

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)